Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/85614
Type: Artigo de evento
Title: Participatory Learning In The Neurofuzzy Short-term Load Forecasting
Author: Hell M.
Costa P.
Gomide F.
Abstract: This paper presents a new approach for shortterm load forecasting using the participatory learning paradigm. Participatory learning paradigm is a new training procedure that follows the human learning mechanism adopting an acceptance mechanism to determine which observation is used based upon its compatibility with the current beliefs. Here, participatory learning is used to train a class of hybrid neurofuzzy network to forecast 24-h daily energy consumption series of an electrical operation unit located at the Southeast region of Brazil. Experimental results show that the neurofuzzy approach with participatory learning requires less computational effort, is more robust, and more efficient than alternative neural methods. The approach is particularly efficient when training data reflects anomalous load conditions or contains spurious measurements. Comparisons with alternative approaches suggested in the literature are also included to show the effectiveness of participatory learning.
Editor: Institute of Electrical and Electronics Engineers Inc.
Rights: fechado
Identifier DOI: 10.1109/CIES.2014.7011848
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84922809626&partnerID=40&md5=b8e5904da2bb92cb936ef7c80deb76cd
Date Issue: 2015
Appears in Collections:Unicamp - Artigos e Outros Documentos

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